import argparse import itertools import math import datetime import logging import json from pathlib import Path import torch import torch.nn.functional as F import torch.utils.checkpoint from accelerate import Accelerator from accelerate.logging import get_logger from accelerate.utils import LoggerType, set_seed from diffusers import AutoencoderKL, DDPMScheduler, DPMSolverMultistepScheduler, PNDMScheduler, UNet2DConditionModel from diffusers.optimization import get_scheduler, get_cosine_with_hard_restarts_schedule_with_warmup from diffusers.training_utils import EMAModel from PIL import Image from tqdm.auto import tqdm from transformers import CLIPTextModel, CLIPTokenizer from slugify import slugify from pipelines.stable_diffusion.vlpn_stable_diffusion import VlpnStableDiffusion from pipelines.util import set_use_memory_efficient_attention_xformers from data.csv import CSVDataModule from training.optimization import get_one_cycle_schedule from models.clip.prompt import PromptProcessor logger = get_logger(__name__) torch.backends.cuda.matmul.allow_tf32 = True torch.backends.cudnn.benchmark = True def parse_args(): parser = argparse.ArgumentParser( description="Simple example of a training script." ) parser.add_argument( "--pretrained_model_name_or_path", type=str, default=None, help="Path to pretrained model or model identifier from huggingface.co/models.", ) parser.add_argument( "--tokenizer_name", type=str, default=None, help="Pretrained tokenizer name or path if not the same as model_name", ) parser.add_argument( "--train_data_file", type=str, default=None, help="A folder containing the training data." ) parser.add_argument( "--instance_identifier", type=str, default=None, help="A token to use as a placeholder for the concept.", ) parser.add_argument( "--class_identifier", type=str, default=None, help="A token to use as a placeholder for the concept.", ) parser.add_argument( "--placeholder_token", type=str, nargs='*', default=[], help="A token to use as a placeholder for the concept.", ) parser.add_argument( "--initializer_token", type=str, nargs='*', default=[], help="A token to use as initializer word." ) parser.add_argument( "--train_text_encoder", action="store_true", default=True, help="Whether to train the whole text encoder." ) parser.add_argument( "--train_text_encoder_epochs", default=999999, help="Number of epochs the text encoder will be trained." ) parser.add_argument( "--tag_dropout", type=float, default=0.1, help="Tag dropout probability.", ) parser.add_argument( "--num_class_images", type=int, default=400, help="How many class images to generate." ) parser.add_argument( "--repeats", type=int, default=1, help="How many times to repeat the training data." ) parser.add_argument( "--output_dir", type=str, default="output/dreambooth", help="The output directory where the model predictions and checkpoints will be written.", ) parser.add_argument( "--embeddings_dir", type=str, default="embeddings_ti", help="The embeddings directory where Textual Inversion embeddings are stored.", ) parser.add_argument( "--seed", type=int, default=None, help="A seed for reproducible training." ) parser.add_argument( "--resolution", type=int, default=768, help=( "The resolution for input images, all the images in the train/validation dataset will be resized to this" " resolution" ), ) parser.add_argument( "--center_crop", action="store_true", help="Whether to center crop images before resizing to resolution" ) parser.add_argument( "--dataloader_num_workers", type=int, default=0, help=( "The number of subprocesses to use for data loading. 0 means that the data will be loaded in the main" " process." ), ) parser.add_argument( "--num_train_epochs", type=int, default=100 ) parser.add_argument( "--max_train_steps", type=int, default=None, help="Total number of training steps to perform. If provided, overrides num_train_epochs.", ) parser.add_argument( "--gradient_accumulation_steps", type=int, default=1, help="Number of updates steps to accumulate before performing a backward/update pass.", ) parser.add_argument( "--gradient_checkpointing", action="store_true", help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.", ) parser.add_argument( "--learning_rate_unet", type=float, default=2e-6, help="Initial learning rate (after the potential warmup period) to use.", ) parser.add_argument( "--learning_rate_text", type=float, default=2e-6, help="Initial learning rate (after the potential warmup period) to use.", ) parser.add_argument( "--scale_lr", action="store_true", default=True, help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.", ) parser.add_argument( "--lr_scheduler", type=str, default="one_cycle", help=( 'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",' ' "constant", "constant_with_warmup", "one_cycle"]' ), ) parser.add_argument( "--lr_warmup_epochs", type=int, default=20, help="Number of steps for the warmup in the lr scheduler." ) parser.add_argument( "--lr_cycles", type=int, default=None, help="Number of restart cycles in the lr scheduler (if supported)." ) parser.add_argument( "--use_ema", action="store_true", default=True, help="Whether to use EMA model." ) parser.add_argument( "--ema_inv_gamma", type=float, default=1.0 ) parser.add_argument( "--ema_power", type=float, default=6/7 ) parser.add_argument( "--ema_max_decay", type=float, default=0.9999 ) parser.add_argument( "--use_8bit_adam", action="store_true", default=True, help="Whether or not to use 8-bit Adam from bitsandbytes." ) parser.add_argument( "--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer." ) parser.add_argument( "--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer." ) parser.add_argument( "--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use." ) parser.add_argument( "--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer" ) parser.add_argument( "--mixed_precision", type=str, default="no", choices=["no", "fp16", "bf16"], help=( "Whether to use mixed precision. Choose" "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10." "and an Nvidia Ampere GPU." ), ) parser.add_argument( "--sample_frequency", type=int, default=100, help="How often to save a checkpoint and sample image", ) parser.add_argument( "--sample_image_size", type=int, default=768, help="Size of sample images", ) parser.add_argument( "--sample_batches", type=int, default=1, help="Number of sample batches to generate per checkpoint", ) parser.add_argument( "--sample_batch_size", type=int, default=1, help="Number of samples to generate per batch", ) parser.add_argument( "--valid_set_size", type=int, default=None, help="Number of images in the validation dataset." ) parser.add_argument( "--train_batch_size", type=int, default=1, help="Batch size (per device) for the training dataloader." ) parser.add_argument( "--sample_steps", type=int, default=15, help="Number of steps for sample generation. Higher values will result in more detailed samples, but longer runtimes.", ) parser.add_argument( "--prior_loss_weight", type=float, default=1.0, help="The weight of prior preservation loss." ) parser.add_argument( "--max_grad_norm", default=1.0, type=float, help="Max gradient norm." ) parser.add_argument( "--noise_timesteps", type=int, default=1000, ) parser.add_argument( "--config", type=str, default=None, help="Path to a JSON configuration file containing arguments for invoking this script." ) args = parser.parse_args() if args.config is not None: with open(args.config, 'rt') as f: args = parser.parse_args( namespace=argparse.Namespace(**json.load(f)["args"])) if args.train_data_file is None: raise ValueError("You must specify --train_data_file") if args.pretrained_model_name_or_path is None: raise ValueError("You must specify --pretrained_model_name_or_path") if args.instance_identifier is None: raise ValueError("You must specify --instance_identifier") if isinstance(args.initializer_token, str): args.initializer_token = [args.initializer_token] if isinstance(args.placeholder_token, str): args.placeholder_token = [args.placeholder_token] if len(args.placeholder_token) == 0: args.placeholder_token = [f"<*{i}>" for i in range(len(args.initializer_token))] if len(args.placeholder_token) != len(args.initializer_token): raise ValueError("Number of items in --placeholder_token and --initializer_token must match") if args.output_dir is None: raise ValueError("You must specify --output_dir") return args def save_args(basepath: Path, args, extra={}): info = {"args": vars(args)} info["args"].update(extra) with open(basepath.joinpath("args.json"), "w") as f: json.dump(info, f, indent=4) def freeze_params(params): for param in params: param.requires_grad = False def make_grid(images, rows, cols): w, h = images[0].size grid = Image.new('RGB', size=(cols*w, rows*h)) for i, image in enumerate(images): grid.paste(image, box=(i % cols*w, i//cols*h)) return grid class AverageMeter: def __init__(self, name=None): self.name = name self.reset() def reset(self): self.sum = self.count = self.avg = 0 def update(self, val, n=1): self.sum += val * n self.count += n self.avg = self.sum / self.count class Checkpointer: def __init__( self, datamodule, accelerator, vae, unet, ema_unet, tokenizer, text_encoder, scheduler, output_dir: Path, instance_identifier, placeholder_token, placeholder_token_id, sample_image_size, sample_batches, sample_batch_size, seed ): self.datamodule = datamodule self.accelerator = accelerator self.vae = vae self.unet = unet self.ema_unet = ema_unet self.tokenizer = tokenizer self.text_encoder = text_encoder self.scheduler = scheduler self.output_dir = output_dir self.instance_identifier = instance_identifier self.placeholder_token = placeholder_token self.placeholder_token_id = placeholder_token_id self.sample_image_size = sample_image_size self.seed = seed or torch.random.seed() self.sample_batches = sample_batches self.sample_batch_size = sample_batch_size @torch.no_grad() def save_model(self): print("Saving model...") unet = self.ema_unet.averaged_model if self.ema_unet is not None else self.accelerator.unwrap_model(self.unet) text_encoder = self.accelerator.unwrap_model(self.text_encoder) pipeline = VlpnStableDiffusion( text_encoder=text_encoder, vae=self.vae, unet=unet, tokenizer=self.tokenizer, scheduler=self.scheduler, ) pipeline.save_pretrained(self.output_dir.joinpath("model")) del unet del text_encoder del pipeline if torch.cuda.is_available(): torch.cuda.empty_cache() @torch.no_grad() def save_samples(self, step, num_inference_steps, guidance_scale=7.5, eta=0.0): samples_path = Path(self.output_dir).joinpath("samples") unet = self.ema_unet.averaged_model if self.ema_unet is not None else self.accelerator.unwrap_model(self.unet) text_encoder = self.accelerator.unwrap_model(self.text_encoder) pipeline = VlpnStableDiffusion( text_encoder=text_encoder, vae=self.vae, unet=unet, tokenizer=self.tokenizer, scheduler=self.scheduler, ).to(self.accelerator.device) pipeline.set_progress_bar_config(dynamic_ncols=True) train_data = self.datamodule.train_dataloader() val_data = self.datamodule.val_dataloader() generator = torch.Generator(device=pipeline.device).manual_seed(self.seed) stable_latents = torch.randn( (self.sample_batch_size, pipeline.unet.in_channels, self.sample_image_size // 8, self.sample_image_size // 8), device=pipeline.device, generator=generator, ) with torch.autocast("cuda"), torch.inference_mode(): for pool, data, latents in [("stable", val_data, stable_latents), ("val", val_data, None), ("train", train_data, None)]: all_samples = [] file_path = samples_path.joinpath(pool, f"step_{step}.jpg") file_path.parent.mkdir(parents=True, exist_ok=True) data_enum = enumerate(data) batches = [ batch for j, batch in data_enum if j * data.batch_size < self.sample_batch_size * self.sample_batches ] prompts = [ prompt.format(identifier=self.instance_identifier) for batch in batches for prompt in batch["prompts"] ] nprompts = [ prompt for batch in batches for prompt in batch["nprompts"] ] for i in range(self.sample_batches): prompt = prompts[i * self.sample_batch_size:(i + 1) * self.sample_batch_size] nprompt = nprompts[i * self.sample_batch_size:(i + 1) * self.sample_batch_size] samples = pipeline( prompt=prompt, negative_prompt=nprompt, height=self.sample_image_size, width=self.sample_image_size, image=latents[:len(prompt)] if latents is not None else None, generator=generator if latents is not None else None, guidance_scale=guidance_scale, eta=eta, num_inference_steps=num_inference_steps, output_type='pil' ).images all_samples += samples del samples image_grid = make_grid(all_samples, self.sample_batches, self.sample_batch_size) image_grid.save(file_path, quality=85) del all_samples del image_grid del unet del text_encoder del pipeline del generator del stable_latents if torch.cuda.is_available(): torch.cuda.empty_cache() def main(): args = parse_args() # if args.train_text_encoder and args.gradient_accumulation_steps > 1 and accelerator.num_processes > 1: # raise ValueError( # "Gradient accumulation is not supported when training the text encoder in distributed training. " # "Please set gradient_accumulation_steps to 1. This feature will be supported in the future." # ) instance_identifier = args.instance_identifier if len(args.placeholder_token) != 0: instance_identifier = instance_identifier.format(args.placeholder_token[0]) now = datetime.datetime.now().strftime("%Y-%m-%dT%H-%M-%S") basepath = Path(args.output_dir).joinpath(slugify(instance_identifier), now) basepath.mkdir(parents=True, exist_ok=True) embeddings_dir = Path(args.embeddings_dir) accelerator = Accelerator( log_with=LoggerType.TENSORBOARD, logging_dir=f"{basepath}", gradient_accumulation_steps=args.gradient_accumulation_steps, mixed_precision=args.mixed_precision ) logging.basicConfig(filename=basepath.joinpath("log.txt"), level=logging.DEBUG) args.seed = args.seed or (torch.random.seed() >> 32) set_seed(args.seed) save_args(basepath, args) # Load the tokenizer and add the placeholder token as a additional special token if args.tokenizer_name: tokenizer = CLIPTokenizer.from_pretrained(args.tokenizer_name) elif args.pretrained_model_name_or_path: tokenizer = CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder='tokenizer') # Load models and create wrapper for stable diffusion text_encoder = CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='text_encoder') vae = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder='vae') unet = UNet2DConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='unet') noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder='scheduler') checkpoint_scheduler = DPMSolverMultistepScheduler.from_pretrained( args.pretrained_model_name_or_path, subfolder='scheduler') vae.enable_slicing() set_use_memory_efficient_attention_xformers(unet, True) set_use_memory_efficient_attention_xformers(vae, True) if args.gradient_checkpointing: unet.enable_gradient_checkpointing() text_encoder.gradient_checkpointing_enable() ema_unet = None if args.use_ema: ema_unet = EMAModel( unet, inv_gamma=args.ema_inv_gamma, power=args.ema_power, max_value=args.ema_max_decay, device=accelerator.device ) # Freeze text_encoder and vae vae.requires_grad_(False) if len(args.placeholder_token) != 0: print(f"Adding text embeddings: {args.placeholder_token}") # Convert the initializer_token, placeholder_token to ids initializer_token_ids = torch.stack([ torch.tensor(tokenizer.encode(token, add_special_tokens=False)[:1]) for token in args.initializer_token ]) num_added_tokens = tokenizer.add_tokens(args.placeholder_token) print(f"Added {num_added_tokens} new tokens.") placeholder_token_id = tokenizer.convert_tokens_to_ids(args.placeholder_token) # Resize the token embeddings as we are adding new special tokens to the tokenizer text_encoder.resize_token_embeddings(len(tokenizer)) token_embeds = text_encoder.get_input_embeddings().weight.data print(f"Token ID mappings:") for (token_id, token) in zip(placeholder_token_id, args.placeholder_token): print(f"- {token_id} {token}") embedding_file = embeddings_dir.joinpath(f"{token}.bin") if embedding_file.exists() and embedding_file.is_file(): embedding_data = torch.load(embedding_file, map_location="cpu") emb = next(iter(embedding_data.values())) if len(emb.shape) == 1: emb = emb.unsqueeze(0) token_embeds[token_id] = emb original_token_embeds = token_embeds.detach().clone().to(accelerator.device) initializer_token_embeddings = text_encoder.get_input_embeddings()(initializer_token_ids) for (token_id, embeddings) in zip(placeholder_token_id, initializer_token_embeddings): token_embeds[token_id] = embeddings else: placeholder_token_id = [] if args.train_text_encoder: print(f"Training entire text encoder.") else: print(f"Training added text embeddings") freeze_params(itertools.chain( text_encoder.text_model.encoder.parameters(), text_encoder.text_model.final_layer_norm.parameters(), text_encoder.text_model.embeddings.position_embedding.parameters(), )) index_fixed_tokens = torch.arange(len(tokenizer)) index_fixed_tokens = index_fixed_tokens[~torch.isin(index_fixed_tokens, torch.tensor(placeholder_token_id))] prompt_processor = PromptProcessor(tokenizer, text_encoder) if args.scale_lr: args.learning_rate_unet = ( args.learning_rate_unet * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes ) args.learning_rate_text = ( args.learning_rate_text * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes ) # Use 8-bit Adam for lower memory usage or to fine-tune the model in 16GB GPUs if args.use_8bit_adam: try: import bitsandbytes as bnb except ImportError: raise ImportError("To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`.") optimizer_class = bnb.optim.AdamW8bit else: optimizer_class = torch.optim.AdamW if args.train_text_encoder: text_encoder_params_to_optimize = text_encoder.parameters() else: text_encoder_params_to_optimize = text_encoder.get_input_embeddings().parameters() # Initialize the optimizer optimizer = optimizer_class( [ { 'params': unet.parameters(), 'lr': args.learning_rate_unet, }, { 'params': text_encoder_params_to_optimize, 'lr': args.learning_rate_text, } ], betas=(args.adam_beta1, args.adam_beta2), weight_decay=args.adam_weight_decay, eps=args.adam_epsilon, ) weight_dtype = torch.float32 if args.mixed_precision == "fp16": weight_dtype = torch.float16 elif args.mixed_precision == "bf16": weight_dtype = torch.bfloat16 def collate_fn(examples): prompts = [example["prompts"] for example in examples] nprompts = [example["nprompts"] for example in examples] input_ids = [example["instance_prompt_ids"] for example in examples] pixel_values = [example["instance_images"] for example in examples] # concat class and instance examples for prior preservation if args.num_class_images != 0 and "class_prompt_ids" in examples[0]: input_ids += [example["class_prompt_ids"] for example in examples] pixel_values += [example["class_images"] for example in examples] pixel_values = torch.stack(pixel_values) pixel_values = pixel_values.to(dtype=weight_dtype, memory_format=torch.contiguous_format) inputs = prompt_processor.unify_input_ids(input_ids) batch = { "prompts": prompts, "nprompts": nprompts, "input_ids": inputs.input_ids, "pixel_values": pixel_values, "attention_mask": inputs.attention_mask, } return batch datamodule = CSVDataModule( data_file=args.train_data_file, batch_size=args.train_batch_size, prompt_processor=prompt_processor, instance_identifier=instance_identifier, class_identifier=args.class_identifier, class_subdir="cls", num_class_images=args.num_class_images, size=args.resolution, repeats=args.repeats, dropout=args.tag_dropout, center_crop=args.center_crop, valid_set_size=args.valid_set_size, num_workers=args.dataloader_num_workers, collate_fn=collate_fn ) datamodule.prepare_data() datamodule.setup() if args.num_class_images != 0: missing_data = [item for item in datamodule.data_train if not item.class_image_path.exists()] if len(missing_data) != 0: batched_data = [ missing_data[i:i+args.sample_batch_size] for i in range(0, len(missing_data), args.sample_batch_size) ] pipeline = VlpnStableDiffusion( text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer, scheduler=checkpoint_scheduler, ).to(accelerator.device) pipeline.set_progress_bar_config(dynamic_ncols=True) with torch.autocast("cuda"), torch.inference_mode(): for batch in batched_data: image_name = [item.class_image_path for item in batch] prompt = [item.prompt.format(identifier=args.class_identifier) for item in batch] nprompt = [item.nprompt for item in batch] images = pipeline( prompt=prompt, negative_prompt=nprompt, num_inference_steps=args.sample_steps ).images for i, image in enumerate(images): image.save(image_name[i]) del pipeline if torch.cuda.is_available(): torch.cuda.empty_cache() train_dataloader = datamodule.train_dataloader() val_dataloader = datamodule.val_dataloader() # Scheduler and math around the number of training steps. overrode_max_train_steps = False num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) if args.max_train_steps is None: args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch overrode_max_train_steps = True num_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch) warmup_steps = args.lr_warmup_epochs * num_update_steps_per_epoch * args.gradient_accumulation_steps if args.lr_scheduler == "one_cycle": lr_scheduler = get_one_cycle_schedule( optimizer=optimizer, num_training_steps=args.max_train_steps * args.gradient_accumulation_steps, ) elif args.lr_scheduler == "cosine_with_restarts": lr_scheduler = get_cosine_with_hard_restarts_schedule_with_warmup( optimizer=optimizer, num_warmup_steps=warmup_steps, num_training_steps=args.max_train_steps * args.gradient_accumulation_steps, num_cycles=args.lr_cycles or math.ceil(math.sqrt( ((args.max_train_steps - warmup_steps) / num_update_steps_per_epoch))), ) else: lr_scheduler = get_scheduler( args.lr_scheduler, optimizer=optimizer, num_warmup_steps=warmup_steps, num_training_steps=args.max_train_steps * args.gradient_accumulation_steps, ) unet, text_encoder, optimizer, train_dataloader, val_dataloader, lr_scheduler = accelerator.prepare( unet, text_encoder, optimizer, train_dataloader, val_dataloader, lr_scheduler ) # Move text_encoder and vae to device vae.to(accelerator.device, dtype=weight_dtype) # Keep text_encoder and vae in eval mode as we don't train these vae.eval() # We need to recalculate our total training steps as the size of the training dataloader may have changed. num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) if overrode_max_train_steps: args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch num_val_steps_per_epoch = len(val_dataloader) num_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch) val_steps = num_val_steps_per_epoch * num_epochs # We need to initialize the trackers we use, and also store our configuration. # The trackers initializes automatically on the main process. if accelerator.is_main_process: config = vars(args).copy() config["initializer_token"] = " ".join(config["initializer_token"]) config["placeholder_token"] = " ".join(config["placeholder_token"]) accelerator.init_trackers("dreambooth", config=config) # Train! total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps logger.info("***** Running training *****") logger.info(f" Num Epochs = {num_epochs}") logger.info(f" Instantaneous batch size per device = {args.train_batch_size}") logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}") logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}") logger.info(f" Total optimization steps = {args.max_train_steps}") # Only show the progress bar once on each machine. global_step = 0 avg_loss = AverageMeter() avg_acc = AverageMeter() avg_loss_val = AverageMeter() avg_acc_val = AverageMeter() max_acc_val = 0.0 checkpointer = Checkpointer( datamodule=datamodule, accelerator=accelerator, vae=vae, unet=unet, ema_unet=ema_unet, tokenizer=tokenizer, text_encoder=text_encoder, scheduler=checkpoint_scheduler, output_dir=basepath, instance_identifier=instance_identifier, placeholder_token=args.placeholder_token, placeholder_token_id=placeholder_token_id, sample_image_size=args.sample_image_size, sample_batch_size=args.sample_batch_size, sample_batches=args.sample_batches, seed=args.seed ) if accelerator.is_main_process: checkpointer.save_samples(0, args.sample_steps) local_progress_bar = tqdm( range(num_update_steps_per_epoch + num_val_steps_per_epoch), disable=not accelerator.is_local_main_process, dynamic_ncols=True ) local_progress_bar.set_description("Epoch X / Y") global_progress_bar = tqdm( range(args.max_train_steps + val_steps), disable=not accelerator.is_local_main_process, dynamic_ncols=True ) global_progress_bar.set_description("Total progress") try: for epoch in range(num_epochs): local_progress_bar.set_description(f"Epoch {epoch + 1} / {num_epochs}") local_progress_bar.reset() unet.train() if epoch < args.train_text_encoder_epochs: text_encoder.train() elif epoch == args.train_text_encoder_epochs: freeze_params(text_encoder.parameters()) sample_checkpoint = False for step, batch in enumerate(train_dataloader): with accelerator.accumulate(itertools.chain(unet, text_encoder)): # Convert images to latent space latents = vae.encode(batch["pixel_values"]).latent_dist.sample() latents = latents * 0.18215 # Sample noise that we'll add to the latents noise = torch.randn_like(latents) bsz = latents.shape[0] # Sample a random timestep for each image timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device) timesteps = timesteps.long() # Add noise to the latents according to the noise magnitude at each timestep # (this is the forward diffusion process) noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps) # Get the text embedding for conditioning encoder_hidden_states = prompt_processor.get_embeddings(batch["input_ids"], batch["attention_mask"]) # Predict the noise residual model_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample # Get the target for loss depending on the prediction type if noise_scheduler.config.prediction_type == "epsilon": target = noise elif noise_scheduler.config.prediction_type == "v_prediction": target = noise_scheduler.get_velocity(latents, noise, timesteps) else: raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}") if args.num_class_images != 0: # Chunk the noise and model_pred into two parts and compute the loss on each part separately. model_pred, model_pred_prior = torch.chunk(model_pred, 2, dim=0) target, target_prior = torch.chunk(target, 2, dim=0) # Compute instance loss loss = F.mse_loss(model_pred.float(), target.float(), reduction="none").mean([1, 2, 3]).mean() # Compute prior loss prior_loss = F.mse_loss(model_pred_prior.float(), target_prior.float(), reduction="mean") # Add the prior loss to the instance loss. loss = loss + args.prior_loss_weight * prior_loss else: loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean") acc = (model_pred == latents).float().mean() accelerator.backward(loss) if not args.train_text_encoder: # Keep the token embeddings fixed except the newly added # embeddings for the concept, as we only want to optimize the concept embeddings if accelerator.num_processes > 1: token_embeds = text_encoder.module.get_input_embeddings().weight else: token_embeds = text_encoder.get_input_embeddings().weight token_embeds.data[index_fixed_tokens, :] = original_token_embeds[index_fixed_tokens, :] if accelerator.sync_gradients: params_to_clip = ( itertools.chain(unet.parameters(), text_encoder.parameters()) if args.train_text_encoder and epoch < args.train_text_encoder_epochs else unet.parameters() ) accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm) optimizer.step() if not accelerator.optimizer_step_was_skipped: lr_scheduler.step() if args.use_ema: ema_unet.step(unet) optimizer.zero_grad(set_to_none=True) avg_loss.update(loss.detach_(), bsz) avg_acc.update(acc.detach_(), bsz) # Checks if the accelerator has performed an optimization step behind the scenes if accelerator.sync_gradients: local_progress_bar.update(1) global_progress_bar.update(1) global_step += 1 if global_step % args.sample_frequency == 0: sample_checkpoint = True logs = { "train/loss": avg_loss.avg.item(), "train/acc": avg_acc.avg.item(), "train/cur_loss": loss.item(), "train/cur_acc": acc.item(), "lr/unet": lr_scheduler.get_last_lr()[0], "lr/text": lr_scheduler.get_last_lr()[1] } if args.use_ema: logs["ema_decay"] = 1 - ema_unet.decay accelerator.log(logs, step=global_step) local_progress_bar.set_postfix(**logs) if global_step >= args.max_train_steps: break accelerator.wait_for_everyone() unet.eval() text_encoder.eval() with torch.autocast("cuda"), torch.inference_mode(): for step, batch in enumerate(val_dataloader): latents = vae.encode(batch["pixel_values"]).latent_dist.sample() latents = latents * 0.18215 noise = torch.randn_like(latents) bsz = latents.shape[0] timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device) timesteps = timesteps.long() noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps) encoder_hidden_states = prompt_processor.get_embeddings(batch["input_ids"], batch["attention_mask"]) model_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample model_pred, noise = accelerator.gather_for_metrics((model_pred, noise)) # Get the target for loss depending on the prediction type if noise_scheduler.config.prediction_type == "epsilon": target = noise elif noise_scheduler.config.prediction_type == "v_prediction": target = noise_scheduler.get_velocity(latents, noise, timesteps) else: raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}") loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean") acc = (model_pred == latents).float().mean() avg_loss_val.update(loss.detach_(), bsz) avg_acc_val.update(acc.detach_(), bsz) if accelerator.sync_gradients: local_progress_bar.update(1) global_progress_bar.update(1) logs = { "val/loss": avg_loss_val.avg.item(), "val/acc": avg_acc_val.avg.item(), "val/cur_loss": loss.item(), "val/cur_acc": acc.item(), } local_progress_bar.set_postfix(**logs) accelerator.log({ "val/loss": avg_loss_val.avg.item(), "val/acc": avg_acc_val.avg.item(), }, step=global_step) local_progress_bar.clear() global_progress_bar.clear() if avg_acc_val.avg.item() > max_acc_val: accelerator.print( f"Global step {global_step}: Validation accuracy reached new maximum: {max_acc_val:.2e} -> {avg_acc_val.avg.item():.2e}") max_acc_val = avg_acc_val.avg.item() if sample_checkpoint and accelerator.is_main_process: checkpointer.save_samples(global_step, args.sample_steps) # Create the pipeline using using the trained modules and save it. if accelerator.is_main_process: print("Finished! Saving final checkpoint and resume state.") checkpointer.save_model() accelerator.end_training() except KeyboardInterrupt: if accelerator.is_main_process: print("Interrupted, saving checkpoint and resume state...") checkpointer.save_model() accelerator.end_training() quit() if __name__ == "__main__": main()